Authors:
Kimia Haghjooei
and
Mansoor Rezghi
Affiliation:
Department of Computer Science, Tarbiat Modares University, Tehran, Iran
Keyword(s):
Adversarial Examples, Adversarial Attack, Video Recognition, Black-Box Attack.
Abstract:
Despite the success of deep learning models, they remain vulnerable to adversarial attacks introducing slight perturbations to inputs, resulting in adversarial examples. Black-box attacks, where model details are hidden from the attacker, gain attention for their real-world applications. Although studying adversarial attacks on video models is crucial due to their surveillance importance and security applications, most works on adversarial examples mainly focus on images, and videos are rarely studied since attacking videos is more challenging. Recent black-box video attacks involve selecting key frames to reduce video’s dimensionality. This addresses the high costs of attacking the entire video but may require numerous queries, making the attack noticeable. Our work introduces QEBB, a query-efficient black-box video attack. We employ an unsupervised key frame selection method to choose frames with vital representative information. Using saliency maps, we focus on key frame salient r
egions. QEBB successfully attacks UCF-101 and HMDB-51 datasets with 100% success and reducing query numbers by nearly 90% in comparison to state-of-the-art methods.
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